Inference Time Context Sparsity: Illusion or Opportunity?
The paper discusses the role of context sparsity in large language model (LLM) efficiency. It argues that the constraints of compute and memory in attention mechanisms are artificial and that extreme context sparsity could enhance LLM inference. The authors provide empirical evidence supporting their position and suggest that current hardware can leverage this sparsity for significant performance gains.
- ▪The paper presents a position that the constraints of compute and memory in LLMs are unnecessary.
- ▪Empirical studies show that current LLMs are robust to inference-time decode sparsity across various tasks.
- ▪Sparse decode kernels can accelerate large-context processing by up to 10x at high sparsity levels on existing hardware.
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Computer Science > Artificial Intelligence arXiv:2605.24168 (cs) [Submitted on 22 May 2026] Title:Inference Time Context Sparsity: Illusion or Opportunity? Authors:Sahil Joshi, Prithvi Dixit, Agniva Chowdhury, Anshumali Shrivastava, Joseph E. Gonzalez, Ion Stoica, Kumar Krishna Agrawal, Aditya Desai View a PDF of the paper titled Inference Time Context Sparsity: Illusion or Opportunity?, by Sahil Joshi and 7 other authors View PDF HTML (experimental) Abstract:Sparsity has long been a central theme in LLM efficiency, but its role in context processing remains unresolved. As LLM workloads shift toward longer contexts and agentic interactions, the compute and memory bottlenecks of attention become increasingly critical, raising the question of whether these constraints are fundamental.
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